A new approach to improve absolute positioning accuracy of robot manipulators

This article describes a new calibration system for robot manipulators which improves their absolute positioning accuracy by using parameter-estimation algorithms based on the Newton method. When 3D position data of the specified points on a manipulator and the joint encoder values are input to the calibration system, the system estimates the offset values of joint encoders, link lengths, and position and orientation of the manipulator base coordinate system with respect to the world coordinate system which is difficult to obtain by conventional calibration methods. This calibration system can be applied to various manipulator types by just changing the basic kinematic equations. The system employs an algebraic programming system called REDUCE to automatically reduce the manipulator kinematic equation and partial differential calculus in the Newton method. For efficiency, first only the arm part with three degrees of freedom and then the hand part are calibrated. The experimental results demonstrate the effectiveness of this system by reducing the robot's absolute positioning errors to the order of repeatability errors.     

Virtual manipulator-based binocular stereo vision positioning system and errors modelling

There is a bottleneck of mobile robots positioning technologies for uncertain goals in complex field environment. Owing to the disturbance of the environment, the objects are hard to be located precisely by robot manipulator. Aiming at the positioning problem, binocular stereo vision system and positioning principle of the picking manipulator in virtual environment (VE) were proposed and expatiated upon; in addition, the manipulator positioning model was built in VE, and the manipulator positioning simulation system was developed by Microsoft Visual C++ 6.0; and the binocular stereo vision system platform with a three-coordinate guideway positioning was constructed for test; what’s more the error sources of vision positioning system was analyzed, and the camera system error was established; the mathematical model of experimental error and the camera calibration matching error were also found; with the developed robot manipulator positioning simulation software and vision system hardware, an experimental platform of positioning system was constructed, and using the platform, the stereo vision data was mapped to the manipulator and was guiding the accurate positioning in VE. Finally, experiment of positioning error compensation was carried out. Results of simulation in VE and the experiment showed that the vision positioning method was feasible for positioning in the field environment; it can be applied to control robot operation and to correct the positioning errors in real-time, especially to the long-range precision modelling and error compensation of robots.     

Estimation model for kinematic calibration of manipulators with a parallel structure

This article provides an estimation model for calibrating the kinematics of manipulators with a parallel geometrical structure. Parameter estimation for serial link manipulators is well developed, but fail for most structures with parallel actuators, because the forward kinematics is usually not analytically available for these. We extend parameter estimation to such parallel structures by developing an estimation method where errors in kinematical parameters are linearly related to errors in the tool pose, expressed through the inverse kinematics, which is usually well known. The method is based on the work done to calibrate the MultiCraft robot. This robot has five linear actuators built in parallel around a passive serial arm, thus making up a two-layered parallel-serial manipulator, and the unique MultiCraft construction is reviewed. Due to the passive serial arm, for this robot conventional serial calibration must be combined with estimation of the parameters in the parallel actuator structure. The developed kinematic calibration method is verified through simulations with realistic data and real robot kinematics, taking the MultiCraft manipulator as the case. © 1994 John Wiley & Sons, Inc.     

基于视觉识别的机械手无抖振定位控制

采用当前方法对机械手进行定位控制时,定位控制所用的时间较长,控制结果与预期结果之间的误差较大,存在定位控制效率低和控制精准度低的问题。将视觉识别技术应用到机械手定位控制中,提出基于视觉识别的机械手无抖振定位控制方法,对采集得到的机械手图像做DCT变化处理,将机械手图像变换到频率域中,采用高斯同态滤波去除机械手图像中存在的噪声,将高斯模型作为分割模型对去噪后的机械手图像做分割处理,得到机械手的轮廓区域。结合Softmax分类器和稀疏自编码网络对机械手进行无抖振定位控制,采用贪婪训练算法根据顺序对网络进行训练,将训练得到的特征点输入Softmax分类器中对机械手进行微调,实现机械手的无抖振定位控制。仿真结果表明,所提方法的控制效率高、控制精准度高。     

机器人的位姿标定及其误差补偿

本文用建立机器人目标空间转换矩阵的方法,通过对机器人几点位姿的标定,从而补偿这几点及以这几点为中心的小区域的误差.这种方法简便实用,仅用标定和增加一些软件的方法可使工业机器人位姿精度大大提高.     

基于三步法的机械臂轨迹跟踪控制

考虑机械臂末端轨迹跟踪控制问题,以跟踪逆运动学求解出的末端期望轨迹对应的各关节期望角度为控制目标.设计了一种基于三步法的控制器,该控制器由类稳态控制、可变参考前馈控制和误差反馈控制3部分组成.证明了该控制器可以通过控制机械臂的各关节力矩实现各关节实际角度对期望角度的状态跟踪,进而使得末端轨迹渐近跟踪期望轨迹,并且跟踪误差是输入到状态稳定的.仿真表明基于三步法控制器的空间机械臂末端可以渐近跟踪期望轨迹,并且该算法可以克服系统的末端负载质量变化等不确定性的影响.     

多新息抗差-自适应卡尔曼滤波定位算法研究

针对移动机器人在定位过程中,由传感器测量误差和机器人模型引起的位姿误差导致系统定位精度急剧下降的问题,提出了一种多新息卡尔曼滤波算法.在标准卡尔曼滤波的基础上,当传感器测量值存在误差时,引入抗差权因子,通过改变误差测量值的权值提高滤波器的估计精度;当机器人位姿存在误差时,引入自适应因子,通过调整状态协方差矩阵的大小抵制位姿误差引起的滤波发散.同时,引入了多新息,即多个时刻的新息向量,进一步提高此非线性系统的精度.实验表明:当存在测量误差和位姿误差时,该滤波算法能有效提高定位精度.     

具有H_∞跟踪性能的机器人自适应PID控制

针对非线性不确定机器人系统的轨迹跟踪控制问题,提出一种鲁棒自适应PID控制算法.该控制器由主控制器和监督控制器组成.主控制器以常规PID控制为基础,基于滑模控制思想设计PID参数的自适应律,根据误差实时修正PID参数.基于Lyapunov函数设计的监督控制器补偿自适应PID控制器与理想控制器之间的差异,使系统具有设定的H_∞的跟踪性能.最后,两关节机器人的仿真实验结果表明了算法的有效性.

Abstract：

A robust adaptive PID control algorithm is proposed for trajectory tracking of robot manipulators with nonlinear uncertainties.The controller is composed of a main controller and a supervisory controller.The main controller is designed based on the traditional PID controller.The parameters of the PID controller are updated online according to the system running errors with the adaptation law based on the sliding mode control.The supervisory controller is proposed to compensate the error between the adaptive PID controller and the ideal controller in the sense of the Lyapunov function with the specified H_∞ tracking performance.Finally, the simulation results based on a two-joint robot manipulator show the effectiveness of the presented controller.     

基于机器视觉的非特定物体的智能抓取系统的研究

针对传统机械臂局限于按既定流程对固定位姿的特定物体进行机械化抓取,设计了一种基于机器视觉的非特定物体的智能抓取系统;系统通过特定的卷积神经网络对深度相机采集到的图像进行目标定位,并在图像上预测出一个该目标的可靠抓取位置,系统进一步将抓取位置信息反馈给机械臂,机械臂根据该信息完成对目标物体的抓取操作;系统基于机器人操作系统,硬件之间通过机器人操作系统的话题机制传递必要信息;最终经多次实验结果表明,通过改进的快速搜索随机树运动规划算法,桌面型机械臂能够根据神经网络模型反馈的的标记位置对不同位姿的非特定物体进行实时有效的抓取,在一定程度上提高了机械臂的自主能力,弥补了传统机械臂的不足.     

一种基于总线的移动探测机器人控制系统设计与实现

针对机械臂定位相关的控制算法缺乏合适验证平台的问题,将移动机器人技术和机械臂控制技术相结合,在分布式控制系统结构的基础上设计并实现了移动探测机器人原型样机。该系统由一个中央主控单元和多个从控制单元构成,单元之间通过总线相连,以实现探测机械臂各部分的模块化。该控制系统已经成功应用于实验室的探测机器人中。该设计能够完成对机械臂运动控制任务,可以用于移动探测机器人平台的建立以及相关算法的实现。